Generalized structured component analysis (GSCA) has been extensively enhanced in terms of data-analytic capability and flexibility as well as computational efficiency. This article illustrates a novel application of GSCA for brain connectivity research, the purpose of which is to facilitate its uses with functional neuroimaging data among applied researchers and practitioners. Using data collected during encoding of source memory in a functional magnetic resonance imaging study, this article demonstrates how to specify and evaluate a fully and bidirectionally connected structural model of brain connectivity using GSCA. Implications of the GSCA approach and future directions for brain research are discussed.
Bibliographical notePublisher Copyright:
© 2019, The Behaviormetric Society.
All Science Journal Classification (ASJC) codes
- Applied Mathematics
- Clinical Psychology
- Experimental and Cognitive Psychology